In [2]:
from shared import graph, stats_utils
from src_draft.utils import LOW_IMP_FEATURES
import shared.ml_config_core as ml_config_core
import pandas as pd
from shared.ml_config_core import ModelConfigsCollection
from shared.ml_config_runner import run_tuning_for_configs_collection
from shared.definitions import TuningResult
import numpy as np
import statsmodels.api as sm
from sklearn.metrics import roc_auc_score, accuracy_score, log_loss
from pandas import CategoricalDtype
from Draft import feature_builder_v2
import importlib
from matplotlib import pyplot as plt
import src_draft.utils as shared_utils
import seaborn as sns
In [24]:
importlib.reload(shared_utils)
shared_utils.pandas_config(pd)
shared_utils.plt_config(plt)
sns.set_theme(style="darkgrid", palette="pastel")
plt.style.use("fivethirtyeight")
In [164]:
importlib.reload(feature_builder_v2)
features_matrix = feature_builder_v2.load_datasets_and_prepare_features(drop_meta_data=True,
ds_type=feature_builder_v2.DatasetType.BASE)
Full DS size: 307511
In [163]:
In [5]:
conditions = [
features_matrix["PrevRatioRejectedAccepted"].isna(),
features_matrix["PrevRatioRejectedAccepted"] == 0,
features_matrix["PrevRatioRejectedAccepted"] <= 0.25,
features_matrix["PrevRatioRejectedAccepted"] > 0.25
]
conditions_2 = [
features_matrix["PrevRatioRejectedAccepted"].isna(),
features_matrix["PrevRatioRejectedAccepted"] == 0,
features_matrix["PrevRatioRejectedAccepted"] > 0,
]
choices = ["No Previous App.", 'All Accepted', "< 25% Rejected", "> 25% Rejected"]
choices_2 = ["No Previous App.", 'All Accepted', "> 0% Rejected"]
# choices = ['All Accepted', "> 0 Rejected"]
# choices = ['No Previous', '0', '> 0']
features_matrix["PrevRatioRejectedAccepted_cats"] = np.select(conditions, choices, default='No Previous App')
features_matrix["PrevRatioRejectedAccepted_cats_2"] = np.select(conditions, choices, default='No Previous App')
features_matrix["PrevRatioRejectedAccepted_cats"] = features_matrix["PrevRatioRejectedAccepted_cats"].astype("category")
features_matrix["PrevRatioRejectedAccepted_cats_2"] = features_matrix["PrevRatioRejectedAccepted_cats_2"].astype(
"category")
In [5]:
In [25]:
stats_utils.nan_summary(features_matrix[["PrevRatioRejectedAccepted"]])
Out[25]:
| Total NaN Values | Proportion NaN (%) | |
|---|---|---|
| PrevRatioRejectedAccepted | 16847 | 5.0 |
Exploratory Analysis¶
This notebooks includes the analysis of selected variables (based on their importance at predicting the target variable) and their relationships. Individual analysis of each variable is available in the EDA_appendices notebook.
In [7]:
add_features = ["PrevRatioRejectedAccepted_cats", "PrevRatioRejectedAccepted_cats_2", "TARGET"]
features_matrix_only_high_imp = features_matrix[shared_utils.HIGH_IMP_FEATURES + add_features]
features_matrix_any_imp = features_matrix[shared_utils.ANY_IMP_FEATURES + add_features]
Dataset Summary¶
NaN Values by Column:
In [26]:
# TODO impute missing values, either
stats_utils.nan_summary(features_matrix_only_high_imp)
Out[26]:
| Total NaN Values | Proportion NaN (%) | |
|---|---|---|
| ExtSource2 | 660 | 0.0 |
| ExtSource3 | 60965 | 20.0 |
| ExtSource1 | 173378 | 56.0 |
| AmtGoodsPrice | 278 | 0.0 |
| OwnCarAge | 202929 | 66.0 |
| PrevAmtDownPaymentSum | 16454 | 5.0 |
| AmtAnnuity | 12 | 0.0 |
| MeanbureaudaysCredit | 44020 | 14.0 |
| MeanbureauamtCreditSumDebt | 51380 | 17.0 |
| PrevAvgYieldGroup | 18945 | 6.0 |
| PrevCreditReceivedRequestedDiff | 16454 | 5.0 |
| OccupationType | 96391 | 31.0 |
| PrevRatioRejectedAccepted | 16847 | 5.0 |
| MaxbureaudaysCreditEnddate | 46269 | 15.0 |
| PrevLastLoanGoodsCategory | 16454 | 5.0 |
| MeanbureauamtCreditMaxOverdue | 123625 | 40.0 |
Duplicates Values¶
In [29]:
duplicated_row_count = features_matrix[features_matrix.duplicated(keep=False)].shape[0]
display(f"Duplicated Values: {duplicated_row_count}")
'Duplicated Values: 0'
In [33]:
display(f"Total Columns: {len(features_matrix.columns)}")
'Total Columns: 229'
Correlations¶
Because we has such a large number of columns we have only included features whhich have an importance value { > X } with our final LGBM model: TODO
In [9]:
# TODO impute missing values (mean for numerical, proportion sampling for cat)
# OR inside correlation check just drop rows with missing values for tested columns
importlib.reload(graph)
features_matrix_any_imp_no_nan = features_matrix_only_high_imp.dropna(axis=0, how="any")
features_matrix_any_imp_no_nan = features_matrix_any_imp_no_nan.apply(
lambda col: col.astype(float) if col.dtype == 'Float64' else col.astype(int) if col.dtype == 'Int64' else col)
graph.render_corr_matrix_based_on_type(features_matrix_any_imp_no_nan)
V:\projects\ppuodz-ML.4.1\shared\graph.py:1276: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead. corr = round(corr.applymap(pd.to_numeric), 2)
The TARGET variable (loans with payment difficulties) is most correlated with credit ratings obtained from external sources. The correlation is very weak but still significant.
`` Because the datatypes of features vary we had to use different methods to measure the strength and significance of each pair:
Chi-Squared Test: Assesses independence between two categorical variables. For bool-bool pairs due to categorical nature.
Point Biserial Correlation: Measures correlation between a binary and a continuous variable. For bool-numerical pairs to account for mixed data types.
Spearman's Rank Correlation: Assesses monotonic relationship between two continuous variables. Used for numerical-numerical pairs (for non-normally distributed data).
Since the Chi-Squared test outputs an unbound statistic/value which can't be directly compared to pointbiserialr or Spearman Rank we have converted them to a Cramér's V: value which is normalized between 0 and 1. This was done to make the values in the matrix more uniform however we must note that Cramér's V and Spearman's correlation coefficients are fundamentally different statistics and generally can't be directly compared.
Corelation With the Target Variable¶
Our target variable TARGET show whether the given application had any late payments (value = 1), we can see that no single feature is strongly correlated with it:
In [ ]:
# Hellowd world dfssdf
In [31]:
correlation_results = []
for col in features_matrix_any_imp_no_nan.columns:
if col == "TARGET":
continue
x = features_matrix_any_imp_no_nan["TARGET"]
y = features_matrix_any_imp_no_nan[col]
corr_value, p_value = stats_utils.correlation_test(x, y)
# if p_value < 0.05:
correlation_results.append({'Column': col, 'Coefficient': corr_value, 'P-Value': p_value})
#
correlation_df = pd.DataFrame(correlation_results).set_index('Column')
correlation_df = correlation_df.loc[correlation_df['Coefficient'].abs().sort_values(ascending=False).index]
correlation_df.round(3)
Out[31]:
| Coefficient | P-Value | |
|---|---|---|
| Column | ||
| ExtSource3 | -0.161 | 0.000 |
| ExtSource1 | -0.131 | 0.000 |
| ExtSource2 | -0.128 | 0.000 |
| MeanbureaudaysCredit | 0.093 | 0.000 |
| OccupationType | 0.075 | 0.000 |
| DaysEmployed | 0.074 | 0.000 |
| PrevRatioRejectedAccepted | 0.073 | 0.000 |
| PrevRatioRejectedAccepted_cats_2 | 0.072 | 0.000 |
| PrevRatioRejectedAccepted_cats | 0.072 | 0.000 |
| OrganizationType | 0.069 | 0.000 |
| NameEducationType | 0.067 | 0.000 |
| PrevAmtDownPaymentSum | -0.057 | 0.000 |
| PrevCreditReceivedRequestedDiff | 0.055 | 0.000 |
| DaysBirth | 0.053 | 0.000 |
| PrevLastLoanGoodsCategory | 0.051 | 0.000 |
| OwnCarAge | 0.050 | 0.000 |
| MeanbureauamtCreditSumDebt | 0.049 | 0.000 |
| MeanbureauamtCreditMaxOverdue | 0.044 | 0.000 |
| DaysIdPublish | 0.042 | 0.000 |
| CodeGender | 0.041 | 0.000 |
| PrevAvgYieldGroup | 0.040 | 0.000 |
| FlagDocument3 | 0.039 | 0.000 |
| AmtGoodsPrice | -0.034 | 0.000 |
| MaxbureaudaysCreditEnddate | 0.034 | 0.000 |
| NameFamilyStatus | 0.027 | 0.002 |
| AmtCredit | -0.023 | 0.001 |
| AmtAnnuity | 0.003 | 0.664 |
In [34]:
features_matrix_only_imp_cat_cols = features_matrix_only_high_imp.select_dtypes(include='category').columns
features_matrix_target_cat = features_matrix_only_high_imp.copy()
features_matrix_target_cat["TARGET"] = features_matrix_target_cat["TARGET"].map(
lambda x: "Default/Loan With Issues" if x == 1 else "No Issues")
The chart below shows the relationship between selected categorical variables and loan status. E.g. a significantly higher proportion of loans taken out by males had issues.
In [36]:
importlib.reload(graph)
graph.draw_distribution_pie_charts(
features_matrix_target_cat,
split_var="TARGET",
include_cols=features_matrix_only_imp_cat_cols,
title="Distribution of Categorical Variables Relative to Loan Risk",
clean_tick_label=False,
)
In [37]:
features_matrix_with_bins = features_matrix_only_high_imp.copy()
numerical_cols = features_matrix_only_high_imp.select_dtypes(
include=["int64", "float64", "Int64"]
).columns
for col in numerical_cols:
if features_matrix_with_bins[col].nunique() < 5:
features_matrix_with_bins[f"{col}_binned"] = features_matrix_with_bins[col].astype("category")
else:
features_matrix_with_bins[f"{col}_binned"] = stats_utils.bin_and_label(
features_matrix_with_bins[col], num_bins=4
)
features_matrix_with_bins[col] = features_matrix_with_bins[col]
import numpy as np
conditions = [
features_matrix["TotalDefaults"] == 0,
features_matrix["TotalDefaults"] >= 1,
# features_matrix["TotalDefaults"] > 1
]
choices = ["No Defaults", '1 Defaulted Loans'] #,"> 1 defaulted loan"]
# choices = ['All Accepted', "> 0 Rejected"]
# choices = ['No Previous', '0', '> 0']
features_matrix_with_bins["TotalDefaults_cats"] = np.select(conditions, choices, default='WTF?').astype("object")
features_matrix_with_bins["Defaulted"] = features_matrix_with_bins["TARGET"].map(lambda x: "Yes" if x == 1 else "No")
features_matrix_with_bins.drop(columns=["TARGET", "TARGET_binned"], inplace=True)
In [38]:
features_matrix_with_bins["PrevRatioRejectedAccepted_cats"].dtype
Out[38]:
CategoricalDtype(categories=['< 25% Rejected', '> 25% Rejected', 'All Accepted', 'No Previous App.'], ordered=False, categories_dtype=object)
Relationships Between Numerical and Categorical Variables¶
The charts below show pairs of numerical and categorical features (including some binned numerical features) that have a signficant relationships and at least a small effect size (eta_squared>0.01) based on the non-parametric Kruskal-Wallis Test (one-way ANOVA on ranks) testing whether samples originate from the same distribution.
*It's similar to the Mann–Whitney U test but allows comparing more than 2 groups
In [39]:
importlib.reload(graph)
for target_y in ["ExtSource2", "AmtCredit", "DaysEmployed"]:
for c in features_matrix_with_bins.columns:
if pd.api.types.is_numeric_dtype(features_matrix_with_bins[c]):
continue
if target_y in c and "binned" in c:
continue
if "ExtSource" in target_y and "ExtSource" in c:
continue
if features_matrix_with_bins[c].nunique() > 10:
continue
res = graph.boxen_plot_by_cat(c, features_matrix_with_bins, target_y)
if res:
display(res)
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target]
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1477: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1490: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
Analyzing Credit Scores (ExtSource1)¶
ExtSource1/2/3 are the variables most strongly correlated with the target variable, they indicate client credit scores obtained from external sources. While th correlation coeficients are very low (only slightly above 0.1) we'll look a bit more into these scores because ussually credit ratings tend be the most useful metric when estimating the risk of specific loans:
In [40]:
import numpy as np
import statsmodels.api as sm
from sklearn.metrics import roc_auc_score, accuracy_score, log_loss
# Plot setup
plt.figure(figsize=(12, 6))
line_styles = ['--', ':', '-.']
x_range = np.linspace(features_matrix[['ExtSource1', 'ExtSource2', 'ExtSource3']].min().min(),
features_matrix[['ExtSource1', 'ExtSource2', 'ExtSource3']].max().max(), 100)
# Initialize lists for storing predictions
predictions = {}
colors = plt.cm.get_cmap('tab10', 4)
for i, source in enumerate(['ExtSource1', 'ExtSource2', 'ExtSource3']):
subset = features_matrix[[source, 'TARGET']].dropna()
X = sm.add_constant(subset[source])
y = subset['TARGET']
model = sm.Logit(y, X).fit(disp=0)
X_pred = pd.DataFrame({'const': 1, source: x_range})
y_pred = model.predict(X_pred)
predictions[source] = y_pred
plt.plot(x_range, y_pred, color=colors(i), linestyle=line_styles[i], alpha=0.5, label=f'{source} (individual)')
combined_features = features_matrix[['ExtSource1', 'ExtSource2', 'ExtSource3', 'TARGET']].dropna()
X_combined = sm.add_constant(combined_features[['ExtSource1', 'ExtSource2', 'ExtSource3']])
y_combined = combined_features['TARGET']
model_combined = sm.Logit(y_combined, X_combined).fit(disp=0)
X_pred_combined = pd.DataFrame({'const': 1, 'ExtSource1': x_range, 'ExtSource2': x_range, 'ExtSource3': x_range})
y_pred_combined = model_combined.predict(X_pred_combined)
y_pred_combined_for_metrics = model_combined.predict(X_combined)
predictions['Combined'] = y_pred_combined
residuals_combined = y_combined - model_combined.predict(X_combined)
residual_std_combined = np.std(residuals_combined)
combined_color = colors(3)
plt.plot(x_range, y_pred_combined, color=combined_color,
label='Combined - Predicted Default Probability')
plt.fill_between(x_range, y_pred_combined - residual_std_combined, y_pred_combined + residual_std_combined,
color=combined_color, alpha=0.2)
auc_combined = roc_auc_score(y_combined, y_pred_combined_for_metrics)
accuracy_combined = accuracy_score(y_combined, y_pred_combined_for_metrics.round()) # Assuming binary classification
logloss_combined = log_loss(y_combined, y_pred_combined_for_metrics)
metrics = f"AUC: {auc_combined:.2f}, Accuracy: {accuracy_combined:.2f}, Log-loss: {logloss_combined:.2f}"
plt.annotate(metrics, xy=(0.01, -0.175), xycoords='axes fraction', fontsize=14, color='black')
plt.title('Predicted Probability of Default by Credit Score Source\n(Logit)')
plt.xlabel('Normalized Credit Score')
plt.ylabel('Probability of Default')
plt.legend()
plt.show()
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\2151574185.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
colors = plt.cm.get_cmap('tab10', 4)
In [52]:
print(f"Summary for combined model:\n{model_combined.summary()}\n") # Display summary
Summary for combined model:
Logit Regression Results
==============================================================================
Dep. Variable: TARGET No. Observations: 109589
Model: Logit Df Residuals: 109585
Method: MLE Df Model: 3
Date: Mon, 29 Apr 2024 Pseudo R-squ.: 0.1047
Time: 19:57:19 Log-Likelihood: -25636.
converged: True LL-Null: -28634.
Covariance Type: nonrobust LLR p-value: 0.000
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const 0.6002 0.040 14.829 0.000 0.521 0.680
ExtSource1 -2.0989 0.061 -34.382 0.000 -2.219 -1.979
ExtSource2 -1.9640 0.060 -32.654 0.000 -2.082 -1.846
ExtSource3 -2.7793 0.062 -44.483 0.000 -2.902 -2.657
==============================================================================
This is a simple Logistic model that just uses the credit scores to estimate the target variable. The confidence interval shows the the standard deviation of the residuals from a combined logistic regression model (residuals in this context are the differences between the observed values (y_combined) and the predicted probabilities).
Gennerally the explained variabity (Pseudo R-squared) is very quite low at only 0.1047 however the model itself is statistically significant (LLR p-value = 0.0)
In [41]:
model_params = model_combined.params
p_values = model_combined.pvalues
conf_int = model_combined.conf_int()
std_errors = model_combined.bse
coeff_df = pd.DataFrame({
'Coefficient': model_params,
'Standard Error': std_errors,
'P-Value': p_values,
'Conf. Interval Lower': conf_int[0],
'Conf. Interval Upper': conf_int[1]
})
coeff_df.round(3)
Out[41]:
| Coefficient | Standard Error | P-Value | Conf. Interval Lower | Conf. Interval Upper | |
|---|---|---|---|---|---|
| const | 0.600 | 0.040 | 0.0 | 0.521 | 0.680 |
| ExtSource1 | -2.099 | 0.061 | 0.0 | -2.219 | -1.979 |
| ExtSource2 | -1.964 | 0.060 | 0.0 | -2.082 | -1.846 |
| ExtSource3 | -2.779 | 0.062 | 0.0 | -2.902 | -2.657 |
Normalized credit ratings from three sources are inversely related to default risk, with ExtSource3 having the strongest influence. We can see that a basic Logistic model can already provide a reasonably high result (AUC = 0.74). However, we have to note that the results are based on the full training set and are only provided for EDA/feature analysis purposes. Full statistical modelling will be done in further sections.
In [ ]:
In [51]:
# Plotting
for i in range(1, 4):
plt.figure(figsize=(12, 6))
col = f'ExtSource{i}'
sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 1][col],
label=f'{col} - Default', shade=True)
sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 0][col], label=f'{col} - No Default', shade=True)
plt.title(f'Density Plot of ExtSource{i} by Default Status')
plt.xlabel('Normalized Credit Score')
plt.ylabel('Density')
plt.legend()
plt.show()
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:6: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:8: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 0][col], label=f'{col} - No Default', shade=True)
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:6: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:8: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 0][col], label=f'{col} - No Default', shade=True)
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:6: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\1233466688.py:8: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['TARGET'] == 0][col], label=f'{col} - No Default', shade=True)
We can see that while the the external credit are clearly related to default risk their explanatory power is somewhat limited because there is still a large amount of overlap (especially for ExtSource2, however it's coeefficient in our logistical model is similar to that of ExtSource1.
In [20]:
from scipy.stats import gaussian_kde
import pandas as pd
import numpy as np
import statsmodels.api as sm
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
df = features_matrix[['ExtSource1', 'ExtSource2', 'ExtSource3', "TARGET", "AmtCredit"]].copy()
# Calculate the average of all ExtSources
df['ExtSourceAvg'] = df[['ExtSource1', 'ExtSource2', 'ExtSource3']].mean(axis=1, skipna=True)
# sources = ['ExtSource1']#, 'ExtSource2', 'ExtSource3', 'ExtSourceAvg']
sources = ['ExtSource1', 'ExtSource2', 'ExtSource3', 'ExtSourceAvg', 'AmtCredit']
for source in sources:
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))
fig.suptitle(f'Analysis for {source}', fontsize=16, y=1.05) # Top-level title
# Separate the data
subset_default = df[df['TARGET'] == 1][source].dropna()
subset_non_default = df[df['TARGET'] == 0][source].dropna()
# Total number of observations with valid data
total_count = len(df[source].dropna())
# Define the range for the KDE
score_range = np.linspace(df[source].min(), df[source].max(), 300)
# KDE for defaults
kde_default = gaussian_kde(subset_default, bw_method='silverman')
density_default = kde_default(score_range) * len(subset_default) / total_count
# KDE for non-defaults
kde_non_default = gaussian_kde(subset_non_default, bw_method='silverman')
density_non_default = kde_non_default(score_range) * len(subset_non_default) / total_count
# Plotting
# TODO: add fill with alpha like kde plots
sns.lineplot(x=score_range, y=density_default, ax=ax1, label='Default Probability')
sns.lineplot(x=score_range, y=density_non_default, ax=ax1, label='Non Default Probability')
ax1.set_title(f'KDE', fontsize=10) # Smaller font size for subplot title
ax1.set_xlabel('Normalized Credit Score')
ax1.set_ylabel('Density')
ax1.legend()
# Regression Plot
subset = df[[source, 'TARGET']].dropna()
sns.kdeplot(
data=subset,
x=source,
hue="TARGET",
# kind="kde",
# height=6,
multiple="fill",
ax=ax2
# clip=(10, 80),
)
# plt.title("Default Rate and EXT_SOURCE_1", x=0.5, y=1.025, fontdict={"size": 16})
ax2.set_xlabel('Normalized Credit Score')
ax2.set_ylabel('Probability of Default')
# ax2.legend()
# ROC AUC as annotation
# roc_auc = roc_auc_score(y, model.predict(X))
# ax2.annotate(f'ROC AUC: {roc_auc:.2f}', xy=(0.05, 0.95), xycoords='axes fraction', fontsize=12, verticalalignment='top')
plt.tight_layout()
plt.show()
Previous Application History¶
Did any clients had previously applied for loans with Home Credit and what were the outcomes of their applications?
In [21]:
features_matrix_with_bins["PrevRatioRejectedAccepted_cats"].value_counts()
Out[21]:
PrevRatioRejectedAccepted_cats All Accepted 190370 > 25% Rejected 66215 < 25% Rejected 34079 No Previous App. 16847 Name: count, dtype: int64
Did any applicants default on any previous loans?
In [22]:
features_matrix_with_bins["TotalDefaults_cats"].value_counts()
Out[22]:
TotalDefaults_cats No Defaults 304114 1 Defaulted Loans 3397 Name: count, dtype: int64
Suprisingly we can see that a ~1% of all applicants who were granted a loans have previously had payment difficulties with a previous loans at Home Credit. This is quite interesting considering that gennerally credit instituions are reluctant to offer loans again to problematic clients.
Total "Defaults"/Loans With Payment Difficulties per applicant:
In [ ]:
In [77]:
total_defaults_df = features_matrix["TotalDefaults"].value_counts().reset_index()
# Calculating the proportion and formatting it to two decimal places
total_defaults_df['proportion'] = (total_defaults_df['count'] / total_defaults_df['count'].sum()).map("{:.3%}".format)
In [73]:
total_defaults_df
Out[73]:
| TotalDefaults | count | proportion | |
|---|---|---|---|
| 0 | 0.0 | 304114 | 0.99 |
| 1 | 1.0 | 3177 | 0.01 |
| 2 | 2.0 | 163 | 0.00 |
| 3 | 3.0 | 38 | 0.00 |
| 4 | 4.0 | 11 | 0.00 |
| 5 | 5.0 | 4 | 0.00 |
| 6 | 6.0 | 3 | 0.00 |
| 7 | 7.0 | 1 | 0.00 |
Previous Loan History and Default Risk¶
The chart below shows the default rate based on whether applicant has previous applied for loans with Home Cred:
No Previous App. - no previous applications for client found (i.e. new clients)
All Accepted - all previous applications were accepted
< 25% Rejected - less than 1/4 applications were rejected
> 25% Rejected - more than 1/4 applications were rejected
In [79]:
In [117]:
In [79]:
features_matrix_with_bins["PrevRatioRejectedAccepted_cats"].value_counts()
Interestingly we can see that while applicants whose previous loans were rejected are significantly more likely to default when finally given a loan previous clients with no failed applications have a higher default risk than new clients.
This likely limits the usefulness of the previous_application table because only a small proportion of clients have previously rejected applications
In [118]:
features_matrix["AnyPreviousRejections"] = features_matrix["PrevRatioRejectedAccepted"] > 0
features_matrix["AnyPreviousDefaults"] = features_matrix["TotalDefaults"] > 0
In [115]:
import seaborn as sns
import matplotlib.pyplot as plt
prop_df = features_matrix.groupby('TARGET')['AnyPreviousRejections'].value_counts(normalize=True).unstack().fillna(0)
plt.figure(figsize=(10, 6))
ax = prop_df.plot(kind='bar', stacked=True, color=['#6baed6', '#3182bd'])
plt.title('Proportion of Clients with Past Rejected Loans by Current Loan Status')
plt.xlabel('Loan Status (TARGET)') # 0 for no default, 1 for default
plt.ylabel('Proportion')
plt.xticks(ticks=[0, 1], labels=['No Default (0)', 'Default (1)'], rotation=0)
for p in ax.patches:
width, height = p.get_width(), p.get_height()
x, y = p.get_xy()
if height > 0:
ax.annotate(f'{height:.1%}', (x + width / 2, y + height / 2), ha='center')
plt.legend(title='Any Previous Default', labels=['No Previous Rejections', 'Previous Rejections'], loc='upper left',
bbox_to_anchor=(1, 1))
plt.show()
<Figure size 1000x600 with 0 Axes>
In [117]:
# Plotting
plt.figure(figsize=(12, 6))
col = f'ExtSource{i}'
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousRejections'] == 1][col],
label=f'{col} - Previous Rejections', shade=True)
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousRejections'] == 0][col], label=f'{col} - No Rejections',
shade=True)
plt.title(f'Density Plot of ExtSource{i} by Previous Application Rejections')
plt.xlabel('Normalized Credit Score')
plt.ylabel('Density')
plt.legend()
plt.show()
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\4178975199.py:5: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousRejections'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\4178975199.py:7: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousRejections'] == 0][col], label=f'{col} - No Rejections', shade=True)
In [121]:
# Plotting
plt.figure(figsize=(12, 6))
# Plotting
for i in range(1, 4):
plt.figure(figsize=(12, 6))
col = f'ExtSource{i}'
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 1][col],
label=f'{col} - Previous Rejections', shade=True)
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 0][col], label=f'{col} - No Rejections',
shade=True)
plt.title(f'Density Plot of ExtSource{i} by Previous Defaults')
plt.xlabel('Normalized Credit Score')
plt.ylabel('Density')
plt.legend()
plt.show()
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:10: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:12: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 0][col], label=f'{col} - No Rejections', shade=True)
<Figure size 1200x600 with 0 Axes>
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:10: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:12: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 0][col], label=f'{col} - No Rejections', shade=True)
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:10: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 1][col],
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\3528939979.py:12: FutureWarning:
`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.
sns.kdeplot(data=features_matrix[features_matrix['AnyPreviousDefaults'] == 0][col], label=f'{col} - No Rejections', shade=True)
We can clearly see that clients who had run into payment issues with their past loans tend to have a signficantly lower credit ExtSource3 however there is almost no difference with other scores. This incidates that the data from Home Credit itself is only included in the third rating (which might explain its higher explantatory power in our Logistic model)
In [145]:
for l in features_matrix.columns:
if "amt" in l.lower():
print(l)
AmtIncomeTotal AmtCredit AmtAnnuity AmtGoodsPrice AmtReqCreditBureauHour AmtReqCreditBureauDay AmtReqCreditBureauWeek AmtReqCreditBureauMon AmtReqCreditBureauQrt AmtReqCreditBureauYear MaxbureauamtAnnuity MaxbureauamtCreditMaxOverdue MaxbureauamtCreditSum MaxbureauamtCreditSumDebt MaxbureauamtCreditSumLimit MaxbureauamtCreditSumOverdue MeanbureauamtAnnuity MeanbureauamtCreditMaxOverdue MeanbureauamtCreditSum MeanbureauamtCreditSumDebt MeanbureauamtCreditSumLimit MeanbureauamtCreditSumOverdue MinbureauamtAnnuity MinbureauamtCreditMaxOverdue MinbureauamtCreditSum MinbureauamtCreditSumDebt MinbureauamtCreditSumLimit MinbureauamtCreditSumOverdue PrevAmtApplicationMean PrevAmtApplicationSum PrevAmtCreditMean PrevAmtCreditSum PrevAmtDownPaymentSum
Loan Purposes¶
In [147]:
graph.boxen_plots_by_category(
source_df=features_matrix,
group_col="NameContractType",
target_col="AmtCredit",
title="Loan Amount by Purpose",
x_label="Loan Amount",
)
V:\projects\ppuodz-ML.4.1\shared\graph.py:1529: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
In [157]:
import seaborn as sns
import matplotlib.pyplot as plt
prop_df = features_matrix.groupby('NameContractType')['TARGET'].value_counts(normalize=True).unstack().fillna(0)
plt.figure(figsize=(10, 6))
ax = prop_df.plot(kind='bar', stacked=True, )
plt.title('Default Risk by Loan Purpose')
plt.xlabel('Loan Type') # 0 for no default, 1 for default
plt.ylabel('Proportion')
for p in ax.patches:
width, height = p.get_width(), p.get_height()
x, y = p.get_xy()
if height > 0:
ax.annotate(f'{height:.1%}', (x + width / 2, y + height / 2), ha='center')
plt.show()
C:\Users\Paulius\AppData\Local\Temp\ipykernel_29624\4000396085.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
prop_df = features_matrix.groupby('NameContractType')['TARGET'].value_counts(normalize=True).unstack().fillna(0)
<Figure size 1000x600 with 0 Axes>
EDA Summary¶
The EDA was performed in paralel with performing feature enginerring (aggregation of non-main tables) and building an initial LGBM model (using all features), to minimize unnecessary complexity only features which have some importance { > X } are included.